
The state of the art for learning disentangled representations is dominated by VAE approaches.
VAEs, or variational auto-encoders, are generative models which re-parametrize a latent space as a distribution to be sampled from.
Compared to other popular generative models such as GANs, the VAE has been shown to be more robust and stable [1]}.
Each dimension of the latent representation learned by the VAE is generally considered to be an independent generative factor [2]}.
These elements in the representation can capture and isolate certain underlying factors without affecting other elements in the latent space.
Although they are generally unsupervised, many authors have observed that the latent factors learned by a VAE often represent disentangled human interpretable concepts.
A great deal of research has been done to further explore the disentanglement of these learned representations[1]}, [4]}, [5]}.
In the works of Kumar et al.[6]} and Chen et al.[7]}, new objective functions are proposed to encourage disentanglement.
In beta-VAE and the later work of Burgess et al.[8]}, [9]}, a variation of the VAE framework is proposed which balances the latent channel capacity and constraints with reconstruction accuracy.
Allowing robust learning of disentangled representations without the trade-off in reconstruction quality.
However, these works generally rely on unsupervised learning making it challenging to impose prior knowledge about the generative factors, or to share information between examples[10]}.
The work of Vowels et al.[11]}, [12]} overturned this paradigm through a weakly-supervised approach which isolates domain knowledge in the training process of a gated VAE.
This framework makes it possible to learn latent subdomains, by appropriately partitioning the training based on shared properties. The learning of the latent factors is still unsupervised, but additional losses are provided as a soft constraint to group the factors into subdomains.
This method was shown to be more informative and has a better quality of disentanglement.
We take inspiration from this and propose an algorithm to learn latent skill and knowledge subdomains.
